QC+AI aims to bring together quantum computing and artificial intelligence communities!

Description of the Workshop

Quantum computers, albeit on a small scale, are becoming more accessible to the public, e.g., through IBM, Google, and D-Wave. Naturally, this calls for exploiting quantum computers to enhance classical Artificial Intelligence (AI), e.g., to improve their prediction performance or enable faster training by exploiting quantum mechanical principles such as superposition and entanglement. To this end, there is a growing interest in quantum artificial intelligence (QAI) to exploit quantum computing (QC) to enhance classical AI techniques. This workshop focuses on seeking contributions encompassing theoretical and applied advances in QAI. On the other hand, there is also an increasing interest in the application of classical AI techniques for solving problems within QC (AI4QC), such as in quantum software engineering, quantum circuit design, and optimizing quantum optimization approaches (e.g., minor embedding in quantum annealing).  Consequently, we also seek contributions that apply classical AI techniques in various aspects of QC. Many AI problems can be cast as optimization problems, and we also welcome contributions formulating AI problems as optimization tasks, e.g., Quadratic Unconstrained Binary Optimization (QUBO) to be solved by quantum annealers.

Topics

• Theoretical foundations of quantum AI algorithms

• Quantum AI applications in any domain, e.g., transportation, chemistry, simulations, physics, etc

• Classical AI techniques in the area of quantum circuit design, such as optimizing quantum circuit compilation and transpilation

• Quantum noise reduction with classical AI techniques

• Classical AI techniques for quantum software engineering, including quantum software testing, debugging, and repair

• Applications of large-language models for quantum circuit design and quantum software engineering

• Quantum AI techniques for quantum software engineering

• Classical AI techniques for optimizing quantum search and optimization algorithms such as QAOA

• Quantum annealing and its applications

• QUBO models of AI problems

• Novel quantum machine learning algorithms such as theory and applications of quantum reservoir computing and quantum extreme learning machines

Format of Workshop

It will be a one-day workshop. We plan to have an invited keynote speaker, paper presentations, and a panel discussion. We may invite additional speakers if the number of paper submissions is low.

Attendance

Everyone interested is welcome!

Submission requirements

The proceedings will be published in the Communications in Computer and Information Science (CCIS) series by Springer. Please follow the CCIS formatting instructions. Find more details at: https://sites.google.com/view/qcai2026/proceedings

Papers do not need to be anonymized.

Full Paper: 9-16 Pages

Work in Progress: 8 pages maximum

Lightning Talks: Only abstract

Submission Site Information: https://easychair.org/conferences?conf=qcai2026

Publication: Accepted papers will be published in a CCIS Springer volume.

Important Deadlines

Submission: October 22, 2025 (AoE)

Notification: November 5, 2025 (AoE)

Camera Ready: December 8, 2025 (AoE) [Strict from the publisher]

Workshop Chairs

1. Shaukat Ali, Simula Research Laboratory, Norway, shaukat@simula.no, 

2. Francisco Chicano, University of Malaga, Spain, chicano@uma.es

3. Alberto Moraglio, University of Exeter, UK, A.Moraglio@exeter.ac.uk

PC Members

• Eneko Osaba, TECNALIA Research & Innovation

• Shunya Minami, National Institute of Advanced Industrial Science and Technology

• Aritra Sarkar, Delft University of Technology

• Gabriel Luque, University of Málaga

• Samuel Yen-Chi Chen, Wells Fargo

• Giovanni Acampora, University of Naples Federico II

• Marco Baioletti, Universita` degli Studi di Perugia i

• Kc Kong, University of Kansas

• James Steck, Wichita State University

• Dheeraj Peddireddy, Purdue University

• Shiho Kim, Yonsei University

• Sebastian Feld, Delft University of Technology

• Paolo Arcaini, National Institute of Informatics

• Mikel Garcia de Andoin, TECNALIA & University of the Basque Country UPV/EHU

• Bilel Derbel, CRIStAL (Univ. Lille)

• Philippe Codognet, JFLI - CNRS / Sorbonne University / University of Tokyo

• Joongheon Kim, Korea University

• Zakaria Abdelmoiz Dahi, University of Malaga

• Shinobu Saito, NTT Corporation

• Junyong Lee , Yonsei University

• Eñaut Mendiluze Usandizaga, Simula Research Laboratory

• Jihong Park, Singapore University of Technology and Design

• Philippe Lacomme, Institut Supérieur d’Informatique, de Modélisation et de leurs Applications 

• Eric Bourreau, LIRMM

• Matthieu Parizy, Fujitsu LTD.

• Ruhan Wang, Indiana University